A sample of Kansas farms was used to examine the relationship between overall efficiency and farm characteristics. Overall efficiency was significantly related to operator age, farm size, and farm type. Approximately 26.7% of the farms were in the top one-third overall efficiency category for more than half of the sample period.Farm Management,

DeLano, Fredrick D.

Langemeier, Michael R.

English

Research Papers in Economics

Characteristics of Highly Efficient Farms
By
Michael R. Langemeier
and
Fredrick D. DeLano
Presented at Western Agricultural Economics Association Annual Meeting
July 11-14, 1999
Fargo, ND
Michael R. Langemeier is an Associate Professor and Fredrick D. DeLano is Administrator for the
Kansas Farm Management Association (KFMA) program, Kansas State University.Characteristics of Highly Efficient Farms
Abstract
A sample of Kansas farms was used to examine the relationship between overall efficiency and
farm characteristics.  Overall efficiency was significantly related to operator age, farm size, and farm
type.  Approximately 26.7% of the farms were in the top one-third overall efficiency category for more
than half of the sample period.1
Introduction
Production costs and profitability vary considerably among farms and ranches.  The profitability
of farms has been examined thoroughly in past research (Fox, Bergen, and Dickson).  Numerous
studies have focused on the magnitude, determinants, and causal relationships of cost control and
profitability.  However, few studies have examined the persistency of performance over time.
Langemeier, Haley, and DeLano examined the consistency of wheat enterprise performance for
Kansas farms.  From 19% to 26% of the farms, depending on the category and region of the state,
were in the top one-third cost and profit categories for 4 or more of the 6 year sample period. 
Research that examines a longer period of time, and the relationship between consistent performance
and farm characteristics is needed.
The objective of this paper is to examine the relationship between overall efficiency and farm
characteristics such as operator age, farm size, and farm type for a sample of Kansas farms.  The paper
will also examine how many years each farm was in the top one-third and bottom one-third overall
efficiency categories and the relationship between persistence in overall efficiency and farm
characteristics. 
Conceptual Framework
Data envelope analysis (DEA) and stochastic frontier methods can be used to estimate relative
efficiency (Coelli, Rao, and Battese).  Stochastic frontier methods effectively account for noise and can
be used to conduct hypotheses tests.  DEA does not require the specification of a functional form for
the production and cost functions, and can more effectively account for multiple outputs.
The DEA approach developed by Fare, Grosskopf, and Lovell and implemented by Chavas2
and Aliber was used in this study.  Technical, allocative, scale, and overall efficiencies can be derived
with this approach.  Technical efficiency measures whether a farm is producing on the production
frontier.  Allocative efficiency measures whether a farm is using the cost-minimizing input mix for a given
output, and scale efficiency measures whether a farm is producing at the most efficient size.  Overall
efficiency is the product of technical, allocative, and scale efficiencies.  The discussion below provides a
summary of how each efficiency measure was computed.
Technical efficiency under variable returns to scale (VRS) was computed by solving the
following linear program for each observation or farm:
Min 1j           (1)
subject to:
Xz # 1j
 xj
yNz > yj
z1 + z2 + ... + zn = 1
zj 0 U+
where 1j
 , a scaling variable used to adjust an input bundle to efficient scale for a fixed output level,
represents technical efficiency for the jth wheat producer; X is a matrix of input levels for each farm; xj
 
is the jth producer’s input levels; z represents a column vector of variable weights; y is a column vector
of fixed output levels; and yj
  is output for the jth wheat producer.
Allocative efficiency (AEj) indices were computed using the following equation:
AEj = (CMj 
v)/(Cj
  TEj) (2)
where Cj
  is the actual cost of production for the jth producer, and CMj
v is minimum cost to produce yj 3
under VRS.   CMj
v was derived by solving the following linear program for each farm:
Min  CMj
v = wjN 0j (3)
subject to:
Xz # 
 0j 
yNz > yj
z1 + z2 + ... + zn = 1
zj 0 U+
where wj
  is a column vector of input prices paid by the jth producer, and 0j  is a cost-minimizing input
bundle for the jth producer.  The VRS were imposed by constraining the sum of z’s (z-sum) to equal 1.
Scale efficiency (SEj
 ) measures were calculated by minimizing total cost under CRS and
scaling the result using minimum cost under VRS:
SEj = CMj
c /CMj
v  (4)
Minimum cost under CRS (CMj
c) was obtained using model (3) with the z-sum unconstrained.
Overall efficiency (OEj) was derived from TE, AE, and SE:
OEj  = TEj x AEj x SEj  (5)
To determine the relationship between overall efficiency and farm characteristics, each
efficiency measure (technical, allocative, scale, and overall efficiency) was first computed using average
input, output, and cost  information for 24 years, 1973 to 1996.  The relationship between overall
efficiency and farm characteristics was then explored using Tobit analysis.  Farm characteristics used in
the analysis included operator age, farm size, and farm type.  Mixed enterprise farms were used as the
default farm type.4
To examine the persistency of overall efficiency, each efficiency measure was computed for
each year in the sample period.  Farms were then separated into top one-third, middle one-third, and
bottom one-third overall efficiency categories.  The number of years each farm was in the top one-third
and bottom one-third overall efficiency categories were used as persistence measures.  Tobit analysis
was used to examine the relationship between persistence and operator age, farm size, and farm type.
Judge et al. suggest using the likelihood ratio test statistic as a summary and goodness of fit
measure for qualitative and limited dependent variable models.  Thus, the likelihood ratio test statistic
was reported for each Tobit regression.
Kansas Farm Management Data
Data for 195 farms in the Kansas Farm Management Associations with continuous data from
1973 to 1996 were used in this study.  These 195 farms represented approximately 7.1% of the total
farms enrolled in the Kansas Farm Management Associations in 1996. 
Data from the Kansas Farm Management Associations were well suited for examining relative
overall efficiency.  Income and expense information was available for each farm.  Income was
expressed on an accrual basis and could be separated into seven categories: grain income (corn,
sorghum, wheat, and other small grains), hay and forage income, cash crop income (soybeans and
sunflowers), beef income (income from cow/calf, backgrounding, and finishing operations), dairy
income, swine income, and other income (income not included in the other six categories).  Livestock
income was reported on a value-added basis.  
Three expense categories were used: labor, purchased inputs, and capital expenses.  Labor
included hired and unpaid operator labor.  Purchased inputs included fuel, utilities, seed, crop5
insurance, fertilizer, lime, herbicide, insecticide, feed, services, and marketing expenses.  Capital
included repairs, machine hire, conservation, interest, rent, taxes, general farm insurance, and
depreciation.  Interest charges on land were computed using the rent to value ratio for dryland, irrigated
land, and pasture in Kansas and individual farm land values.  Interest charges on intermediate assets
were computed using asset values and interest rates.  Following the procedures of the Kansas Farm
Management Associations, a flat labor charge per operator ($22,500 in 1996) and a management
charge of 5% of gross farm income were used to compute unpaid operator labor and management
charges. 
Table 1 presents the averages and standard deviations of selected production and financial
characteristics for the sample of farms.  All financial variables are converted to 1996 dollars using the
implicit price deflator for personal consumption expenditures (U.S. Department of Commerce). 
Average gross farm income was $244,570.  Of this amount, $149,609 was derived from crops and
$94,962 was derived from livestock.  Farm types were computed by examining the percent of income
derived from various sources.  For a farm to be a particular type, over 50% of gross farm income had
to be derived from one source.  Crop production was the most common farm type followed by mixed
enterprise farms (farms that did not derive over 50% of their income from crops, beef, dairy, or swine).
Results
Overall Efficiency
The distribution of the four efficiency measures computed using average input, output, and cost
information is presented in Table 3.  Overall efficiency averaged 0.565.  Thus, on average, cost could
have been reduced by 43.5% if all of the farms were producing on the cost frontier at the constant6
returns to scale level.  Approximately 11.3% of the farms had an overall efficiency index above 0.80
and 2 of the farms were overall efficient.  Farms with an overall efficiency index less than 1 could have
been technically, allocatively, and/or scale inefficient.
Table 4 presents the regression results examining the relationship between efficiency, and age of
operator, farm size, and farm type.  All of the regression variables except the swine farm type were
significant for at least one of the efficiency measures.
Age of operator was negatively related to overall efficiency suggesting that older operators
were less overall efficient than younger operators.  Farm size (gross farm income) was positively related
to overall efficiency.  Beef farms were less overall efficient, and crop and dairy farm were more overall
efficient than the mixed enterprise farm type.
Persistence of Overall Efficiency
The persistence of overall efficiency is presented in Table 4.  Approximately 27.2% of the
farms were in the bottom one-third overall efficiency category for 13 or more of the 24 years. 
Approximately 11.8% of the farms were in this category for 20 or more years.  Approximately 12.8%
of the farms were not in the bottom one-third overall efficiency category for any of the 24 years.
Approximately 26.7% of the farms were in the top one-third overall efficiency category for 13
or more of the 24 years.  Approximately 12.3% of the farms were in this category for 20 or more years
and 5 farms were in this category for each of the 24 years.  On the low end of the distribution, 32 of the
195 farms were not in the top one-third overall efficiency category for any of the 24 years.
  The results in Table 4 suggest that it is possible, even given the wide fluctuations in weather
over time, for farms to be consistently good performers.  It is also important to note that some farms7
have a difficult time staying out of the bottom one-third overall efficiency category.   These farms are
consistently poor performers.
Table 5 presents the Tobit regressions examining the relationship between persistence in overall
efficiency, and age of operator, farm size, and farm type.  Gross farm income was positively related to
the number of years in which a farm was in the top one-third overall efficiency category and negatively
related to the number of years a farm was in the bottom one-third overall efficiency category.  Beef
farms tended to be poor performers while crop and dairy farms tended to be good performers.
Conclusions and Implications
This study examined the overall efficiency and persistence of overall efficiency for a sample of
Kansas farms.  Overall efficiency was significantly related to operator age, farm size, and farm type. 
Younger operators, larger farms, and crop and dairy farms tended to be relatively more overall
efficient.
Approximately one-fourth of the farms were in the top one-third overall efficient category for
13 or more of the 24 years.  Another one-fourth of the farms were in the bottom one-third overall
efficiency category for 13 or more of the 24 years.  Thus, there does seem to be a quite a bit of
persistence in cost efficiency rankings over time.  This result strengthens the results of studies that use
only a few years when conducting efficiency analyses.8
Table 1.  Summary Statistics for a Sample of Kansas Farms.
Variable Unit Average
Standard
Deviation
Income Measures
Crop Income $ 149,609 112,390
Livestock Income $   94,962 109,366
Gross Farm Income $ 244,570 156,779
Cost Measures
Labor $   41,461 25,402
Purchased Inputs $   91,444 73,150
Capital $ 120,800 71,716
Total Expenses $ 253,705 160,247
Farm Characteristics
Age of Operator No. 52.33 7.33
Beef Farms % 12.31 32.94
Dairy Farms % 4.10 19.89
Swine Farms % 4.10 19.89
Crop Farms % 64.10 48.09
Mixed Enterprise Farms % 15.38 36.17
Notes:  Data constructed from 1973-1996 continuous data records maintained by sample farms
participating in the Kansas Farm Management Associations program.  All financial variables are
converted to 1996 dollars.9
Table 2.  Efficiency Measures for a Sample of Kansas Farms.
Variable
Efficiency Measures
Technical Allocative Scale Overall
Summary Statistics
Average 0.733 0.910 0.847 0.565
Standard Deviation 0.173 0.084 0.142 0.178
Distribution of Farms
Less than 0.30 0 0 1 6
0.30 to 0.40 3 0 0 33
0.40 to 0.50 13 0 4 38
0.50 to 0.60 35 1 5 47
0.60 to 0.70 40 2 25 25
0.70 to 0.80 33 23 27 24
0.80 to 0.90 29 40 35 14
0.90 to 1.00 17 119 96 6
1.00 25 10 2 210
Table 3.  Tobit Regression Analysis of Relationships Between Efficiency and Farm Characteristics.
Variable
Efficiency Measures
Technical Allocative Scale Overall
Intercept     0.880042***
(0.097618)
     0.967351***  
 (0.055279)  
0.573651***
(0.057986)   
  0.527845***
(0.083083)  
Age of Operator  -0.003301**
(0.001503)
 0.000271  
(0.000851) 
-0.001304    
(0.000891)   
-0.003381***
(0.001278)  
Gross Farm Income     -6.482 E-07***
 (2.258 E-07)
   -4.834 E-07***
(1.285 E-07)
   1.857 E-06***
(1.340 E-07) 
   5.470 E-07***
(1.917 E-07)
Gross Farm Income 
Squared
   6.156 E-13**
 (2.700 E-13)
   5.181 E-13***
(1.550 E-13)
  -1.764 E-12***
(1.600 E-13)
-5.702 E-13**
(2.290 E-13)
Beef -0.025291   
 (0.039861)  
-0.012667    
(0.022818)   
-0.079464***
(0.023895)   
-0.080242**   
(0.034261)    
Dairy   0.602435***
(0.088472)  
0.072952** 
(0.034473)   
0.032569   
(0.035350)   
0.402654*** 
(0.050557)    
Swine 0.045612  
(0.057759)  
-0.035615    
(0.032994)   
-0.037053     
(0.034660)   
-0.022226     
(0.049695)    
Crop   0.191656***
(0.029745)  
0.008784   
(0.016974)   
0.073100***
  (0.017803)     
0.192634***
(0.025514)   
Likelihood Ratio 
Test Statistic
112.26***                23.30***          194.64***                 
143.20***                 
Notes:  Numbers in parentheses are standard errors.  Single, double, and triple asterisks (*) denote
significance at the 10%, 5%, and 1% levels, respectively.11
Table 4.  Persistence of Overall Efficiency.
Number of Farms in Overall Efficiency Category
Number
of Years
Bottom
One-Third
Top
One-Third
0 25 32
1 21 22
2 14 12
3 9 6
4 14 11
5 13 13
6 13 8
7 6 6
8 4 11
9 10 6
10 4 6
11 5 5
12 4 5
13 5 5
14 5 2
15 1 4
16 4 1
17 5 6
18 2 5
19 8 5
20 8 5
21 7 6
22 2 6
23 4 2
24 2 512
Table 5.  Tobit Regression Analysis of Relationship Between Persistence in Overall Efficiency 
   and Farm Characteristics.
Overall Efficiency Category
Variable
Bottom
One-Third
Top
One-Third
Intercept 11.69590***
(3.71955)  
2.59273
(4.60599)
Age of Operator 0.10619* 
(0.05725)  
-0.10852 
(0.07101)
Gross Farm Income -3.110 E-05***
(8.607 E-06)  
2.643 E-05**
(1.100 E-05)  
Gross Farm Income Squared 3.515 E-11***
(1.030 E-11)   
-2.742 E-11** 
(1.260 E-11)  
Beef 5.91038***
(1.52400)   
-4.02233*
(2.08456)
Dairy -17.68009***   
(3.26096)   
19.39090***
(2.80652)  
Swine 0.39869   
(2.20086)   
0.44275   
(2.79670)   
Crop -8.08979***  
(1.13513)    
9.06965***
(1.43852)   
Likelihood Ratio Test Statistic 150.10***            147.86***         
Notes:  Numbers in parentheses are standard errors.  Single, double, and triple asterisks (*) denote
significance at the 10%, 5%, and 1% levels, respectively.13
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CHARACTERISTICS OF HIGHLY EFFICIENT FARMS

CHARACTERISTICS OF HIGHLY EFFICIENT FARMS

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